|
|
Generating Documentation to Source Code in Python
Novosád, Juraj ; Nosko, Svetozár (referee) ; Smrž, Pavel (advisor)
The aim of this work is to adapt selected language models on domain data and to develop a system that would allow their use on commonly available hardware. The models have been adapted to generate documentation for undocumented source code in the Python progra- mming language to follow the Google Style convention. A prerequisite of model adaptation was to obtain domain data and process it appropriately for the purpose of model fine-tuning. This work focuses on fine-tuning models with fewer than one billion parameters, for the sake of enabling inference even on commonly available hardware. Part of the work was to objectively evaluate the quality of the adapted models. For this reason, I developed a tool that evaluates the quality of the generated documentation on a selected corpus of models. The evaluation of the adapted models showed that they achieve comparable performance to multiply larger models for general tasks, such as gpt-3.5-turbo-0125. The result of this work is a server capable of horizontal scaling that integrates the capabilities of more than just the adapted models through an easy-to-use API.
|
|
|
Application for Text Summarization
Mička, Jakub ; Zendulka, Jaroslav (referee) ; Bartík, Vladimír (advisor)
This work is focused on an implementation a web application, which is a tool for automatic English text summarization. In result, automatic text summarization is made by TextRank and Latent semantic analysis method. Both of these methods are improved by named entity recognition. The main benefit of this work is proving that using the named entity recognition with Latent semantic analysis and especially with TextRank method leads to creation of higher quality summaries. This quality of the summaries was verified by ROUGE metrics.
|
|
|
Multi-source Text Summarization for Czech
Brus, Tomáš ; Bojar, Ondřej (advisor) ; Mareček, David (referee)
This work focuses on the summarization task for a set of articles on the same topic. It discusses several possible ways of summarizations and ways to assess their final quality. The implementation of the described algorithms and their application to selected texts constitutes a part of this work. The input texts come from several Czech news servers and they are represented as deep syntactic trees (the so called tectogrammatical layer).
|
|
|
Application for Text Summarization
Mička, Jakub ; Zendulka, Jaroslav (referee) ; Bartík, Vladimír (advisor)
This work is focused on an implementation a web application, which is a tool for automatic English text summarization. In result, automatic text summarization is made by TextRank and Latent semantic analysis method. Both of these methods are improved by named entity recognition. The main benefit of this work is proving that using the named entity recognition with Latent semantic analysis and especially with TextRank method leads to creation of higher quality summaries. This quality of the summaries was verified by ROUGE metrics.
|
|
|
Multi-source Text Summarization for Czech
Brus, Tomáš ; Bojar, Ondřej (advisor) ; Mareček, David (referee)
This work focuses on the summarization task for a set of articles on the same topic. It discusses several possible ways of summarizations and ways to assess their final quality. The implementation of the described algorithms and their application to selected texts constitutes a part of this work. The input texts come from several Czech news servers and they are represented as deep syntactic trees (the so called tectogrammatical layer).
|
guest ::
